Pre Test analysis

plot_condition = function(condition) {

  fname = paste("../data/0_pre_test-cond", condition, "-trials.csv", sep="")
  
d = read.csv(fname)

drops <- c("modal1","rating1")
d2 = d[ , !(names(d) %in% drops)]
setnames(d2, old=c("rating2","modal2"), new=c("rating", "modal"))

drops <- c("modal2","rating2")
d3 = d[ , !(names(d) %in% drops)]
setnames(d3, old=c("rating1","modal1"), new=c("rating", "modal"))

drops <- c("modal2", "rating2", "modal1", "rating1")
d4 = d[ , !(names(d) %in% drops)]
d4$rating = d4$rating_other
d4$modal = "other"

d = rbind(d2, d3, d4)

d$modal = factor(d$modal)

d$percentage_blue_f = factor(d$percentage_blue)


d_blue = d %>% filter(., grepl("blue", sentence2))
d_orange = d %>% filter(., grepl("orange", sentence2))

#ggplot(d_orange, aes(x=percentage_blue, y=rating)) + geom_point(aes(col=modal)) +
# geom_smooth(aes(col=modal))

#ggplot(d_blue, aes(x=percentage_blue, y=rating)) + geom_point(aes(col=modal)) +  geom_smooth(aes(col=modal))

d_orange_reverse = d_orange
d_orange_reverse$percentage_blue = 100-d_orange$percentage_blue

d_comparison = rbind(d_blue, d_orange_reverse)
d_comparison$blue= grepl("blue", d_comparison$sentence2)
d_comparison$percentage_blue_f = factor(d_comparison$percentage_blue)

d_comparison$modal = gsub('"', "", d_comparison$modal)
d_comparison$pair = gsub('"', "", d_comparison$pair)


d_comparison$modal = factor(d_comparison$modal, levels=modals, labels = modals_labels, ordered = T)

#p1 = ggplot(d_comparison, aes(x=percentage_blue, y=rating)) + geom_point(aes(col=modal)) +  stat_smooth(aes(col=modal, fill=modal),  method = 'loess', geom="area", alpha=0.5) + ggtitle(d$pair[1]) + xlab("percentage") 

#p1 = ggplot(d_comparison, aes(x=percentage_blue, y=rating)) +  stat_smooth(aes(col=modal, fill=modal),  method = 'loess', geom="area", alpha=0.5) + ggtitle(d$pair[1]) + xlab("percentage") 

d_by_modal_col = d_comparison %>% group_by(modal,percentage_blue, blue, pair) %>% summarise(rating_m = mean(rating), ci_low=ci.low(rating), ci_high=ci.high(rating))

d_by_modal_col$modal = factor(d_by_modal_col$modal, levels=modals, labels = modals_labels, ordered = T)



p1 = ggplot(d_by_modal_col, aes(x=percentage_blue, y=rating_m, group=interaction(modal, blue), col=modal, linetype=blue)) + geom_point(aes(col=modal)) + geom_line() + xlab("percentage") +   geom_errorbar(aes(ymin=rating_m-ci_low, ymax=rating_m+ci_high), width=.1) + facet_wrap(~pair) + colscale

p1a = ggplot(d_comparison %>% group_by(modal,percentage_blue, pair) %>% summarise(rating_m = mean(rating), ci_low=ci.low(rating), ci_high=ci.high(rating)), aes(x=percentage_blue, y=rating_m, group=modal, col=modal)) + geom_point(aes(col=modal), size=1) + geom_line(size=1) + xlab("percentage") +   geom_errorbar(aes(ymin=rating_m-ci_low, ymax=rating_m+ci_high), width=5, size=1) + ylab("mean rating") + facet_wrap(~pair) + colscale   + guides(col=guide_legend(title="Expression", nrow = 1)) + theme(legend.position="bottom", legend.text=element_text(size=14))



p2 = ggplot(d_comparison, aes(x=percentage_blue_f, y=rating, fill=modal)) + 
  geom_boxplot() +
  ggtitle(d$pair[1]) + xlab("percentage") + colscale


d_blue = d_comparison %>% 
  group_by(modal,percentage_blue, blue) %>% 
  summarise(rating_m = mean(rating), ci_low=ci.low(rating), ci_high=ci.high(rating)) %>% 
  filter(blue == T)

d_orange = d_comparison %>% 
  group_by(modal,percentage_blue, blue) %>% 
  summarise(rating_m = mean(rating), ci_low=ci.low(rating), ci_high=ci.high(rating)) %>% 
  filter(blue == F)

d_merged = merge(d_blue, d_orange, by=c("modal", "percentage_blue"))

cat(paste("Correlation between blue and orange mean ratings: ", cor(d_merged$rating_m.x, d_merged$rating_m.y), "\n\n"))

return(list("p1" = p1, "p1a" = p1a, "p2" = p2, d=d_comparison))

}

## Correlation between blue and orange mean ratings:  0.996711492638571

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.923030314467

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.863527461787532

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.984261475999208

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.962769617937498

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.928535433206696

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.924658272328862

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.914483963750568

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.919032907430183

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.908121196457727

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.953222364976044

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.9441508902395

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.971467680024331

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.897540620006031

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.968068808126213

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.995746651884078

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.992452816431708

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.98847068781724

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.993370454309582

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.994536262644918

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.

## Correlation between blue and orange mean ratings:  0.991213912307591

## Warning: The labeller API has been updated. Labellers taking `variable`and
## `value` arguments are now deprecated. See labellers documentation.